This repo contains python code for using a pre-trained TensorFlow model to classify the quality (e.g. running inference) of image focus in microscope images.

Code for training a new model from a dataset of in-focus only images is included as well.

This is not an official Google product.

See our paper PDF for reference:

Yang, S. J., Berndl, M., Ando, D. M., Barch, M., Narayanaswamy, A. , Christiansen, E., Hoyer, S., Roat, C., Hung, J., Rueden, C. T., Shankar, A., Finkbeiner, S., & and Nelson, P. (2018). Assessing microscope image focus quality with deep learning. BMC Bioinformatics, 19(1).

Also see the Fiji (ImageJ) Microscope Focus Qualtiy plugin, which allows use of the same pre-trained model on user-supplied images in a user-friendly graphical user interface, without the need to write any code. Fiji also has macro scripting capabilities for running batches of images. This plugin is being actively maintained. I recommend testing your images with the Fiji plugin before investing further effort in this python library.

Finally, please note that this python library was developed on the older and now (as of 2020) deprecated Python 2.7 and TensorFlow 1.x, and is not being actively maintained and updated. The setup.py currently restricts to these older versions. If you just want the pre-trained model for integration with your own inference code, you may need a saved_model.pb file that's currently only distributed with the Fiji plugin and downloadable here. Updating this library to work with Python 3.x may be fairly straight forward. However, updating it to work on TensorFlow 2.x may require quite a bit of refactoring (at the very least, it appears the data provider implementation and interface may need to change).

Clone the main branch of this repository

git clone -b main https://github.com/google/microscopeimagequality.git

Install the package:

cd microscopeimagequality

Note: This requires pip be installed.

Note: This library has not been migrated beyond TensorFlow 1.x

Note: As of now TensorFlow 1.x requires Python 3.7 or earlier.

Note: This library has been tested with Python 3.7.9 (using pyenv).

python --version
python -m pip install --editable .

If using pyenv, run pyenv rehash.

Download the model: This downloads the model.ckpt-1000042 checkpoint (a model trained for 1000042 steps) specified in constants.py.

microscopeimagequality download 

or alternatively:

import microscopeimagequality.miq
microscopeimagequality.miq.download_model()

Add path to local repository (e.g. /Users/user/my_repo/microscopeimagequality) to PYTHONPATH environment variable:

export PYTHONPATH="${PYTHONPATH}:/Users/user/my_repo/microscopeimagequality"

Run all tests to make sure everything works. Install any missing packages (e.g. python -m pip install pytest, then if using pyenv, run pyenv rehash).

pytest --disable-pytest-warnings

You should now be able to run:

microscopeimagequality --help

or directly access the module functions in a jupyter notebook or from your own python module:

python
from microscopeimagequality import degrade
degrade.degrade(...)

• A pre-trained TensorFlow model .ckpt files, downloadable using download instructions above.
• TensorFlow 1.0.0 or higher, numpy, scipy, pypng, PIL, skimage, matplotlib
• Input grayscale 16-bit images, .png of .tif format, all with the same width and height.

(Optional) Confirm that all images are of the same dimension:

 microscopeimagequality validate tests/data/images_for_glob_test/*.tif --width 100 --height 100

Run inference on each image independently.

  microscopeimagequality predict \
  --output tests/output/ \
  tests/data/BBBC006*10.png

Summarize the prediction results across the entire dataset. Output will be in "summary" sub directory.

microscopeimagequality summarize tests/output/miq_result_images/

• TensorFlow 1.0.0 or higher, and several other python modules.
• A dataset of high quality, in-focus images (at least 400+), as grayscale 16-bit images, .png of .tif format, all with the same width and height.

1. Generate additional labeled training examples of defocused images using degrade.py.
2. Launch microscopeimagequality fit to train a model.
3. Launch microscopeimagequality evaluate with a held-out test dataset.
4. Use TensorBoard to view training and eval progress (see evaluation.py).
5. When satisfied with model accuracy, save the model.ckpt files for later use.

Example fit:

microscopeimagequality fit \
	--output tests/train_output \
	tests/data/training/0/*.tif \
	tests/data/training/1/*.tif \
	tests/data/training/2/*.tif \
	tests/data/training/3/*.tif \
	tests/data/training/4/*.tif \
	tests/data/training/5/*.tif \
	tests/data/training/6/*.tif \
	tests/data/training/7/*.tif \
	tests/data/training/8/*.tif \
	tests/data/training/9/*.tif \
	tests/data/training/10/*.tif

Example evaluation:

microscopeimagequality evaluate \
	--checkpoint <path_to_model_checkpoint>/model.ckpt-XXXXXXX \
	--output tests/data/output \
	tests/data/training/0/*.tif \
	tests/data/training/1/*.tif \
	tests/data/training/2/*.tif \
	tests/data/training/3/*.tif \
	tests/data/training/4/*.tif \
	tests/data/training/5/*.tif \
	tests/data/training/6/*.tif \
	tests/data/training/7/*.tif \
	tests/data/training/8/*.tif \
	tests/data/training/9/*.tif \
	tests/data/training/10/*.tif